Human activity monitoring device with activity identification

ABSTRACT

A method for monitoring human activity using an inertial sensor includes monitoring accelerations, identifying a current user activity from a plurality of user activities based on the accelerations, and counting periodic human motions appropriate to the current user activity.

RELATED APPLICATIONS

This patent is a continuation of U.S. patent application Ser. No.12/069,267, filed on Feb. 8, 2008, now U.S. Pat. No. 8,949,070, issuedFeb. 3, 2015, which claims the benefit under 35 U.S.C. § 119(e) of U.S.Provisional Application No. 60/900,412, filed Feb. 8, 2007, both ofwhich are herein incorporated by reference.

FIELD OF THE INVENTION

This invention relates to a method of monitoring human activity, andmore particularly to identifying user activities and counting periodichuman motions appropriate to the identified user activities.

BACKGROUND

The development of Micro-Electro-Mechanical Systems (MEMS) technologyhas enabled manufacturers to produce inertial sensors (e.g.,accelerometers) of sufficiently small size, cost, and power consumptionto fit into portable electronic devices. Such inertial sensors can befound in a limited number of commercial electronic devices such ascellular phones, portable music players, pedometers, game controllers,and portable computers.

Step counting devices (e.g., pedometers) are used to monitor anindividual's daily activity by keeping track of the number of steps thathe or she takes. Steps are counted by sensing accelerations caused byuser motion. Step counting devices are not able to detect or countmotions other than steps. Nor are step counting devices capable ofdifferentiating between different user activities.

Some step counting devices may be attached to and receive input fromexternal sensors (e.g., a bike cadence sensor) to detect a motion otherthan a step. However, such step counting devices rely on externalsensors for such monitoring capability. These devices are not capable ofdetecting or counting motions other than steps without the input fromexternal sensors.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention is illustrated by way of example, and not by wayof limitation, and can be more fully understood with reference to thefollowing detailed description when considered in connection with thefollowing figures:

FIG. 1 is a block diagram illustrating an electronic device, inaccordance with one embodiment of the present invention;

FIG. 2 is a block diagram illustrating a motion identification system,in accordance with one embodiment of the present invention;

FIG. 3A illustrates a first exemplary motion cycle graph that shows auser engaged in a first user activity;

FIG. 3B illustrates a second exemplary motion cycle graph 350 that showsa user engaged in a second user activity;

FIG. 4A illustrates a flow diagram for a method of monitoring humanactivity using an inertial sensor, in accordance with one embodiment ofthe present invention;

FIG. 4B illustrates a flow diagram for a method of recording a motionprofile, in accordance with one embodiment of the present invention;

FIG. 5 shows a state diagram for the behavior of a system for monitoringhuman activity, in accordance with one embodiment of the presentinvention;

FIG. 6 illustrates a flow diagram for a method of operating anelectronic device in sleep mode, in accordance with one embodiment ofthe present invention;

FIG. 7A illustrates a flow diagram for a method of operating anelectronic device in entry mode, in accordance with one embodiment ofthe present invention;

FIG. 7B illustrates a flow diagram for a method of operating anelectronic device in entry mode, in accordance with another embodimentof the present invention;

FIG. 8 illustrates a flow diagram for a method of operating anelectronic device in an active mode, in accordance with one embodimentof the present invention;

FIG. 9 illustrates a flow diagram for a method of operating anelectronic device in an exit mode, in accordance with one embodiment ofthe present invention;

FIG. 10 illustrates a flow diagram for a method of recognizing aperiodic human motion, in accordance with one embodiment of the presentinvention;

and

FIG. 11 illustrates a block diagram of a machine in the exemplary formof a computer system within which a set of instructions may be executedin accordance with an embodiment of the present invention.

DETAILED DESCRIPTION

Embodiments of the present invention are designed to monitor humanactivity using an inertial sensor. In one embodiment, accelerations aremonitored, and a current user activity is identified from multiplerecognizable user activities based on the accelerations. Theseactivities include any activities based on periodic human motions,including, for example: walking, inline skating, bicycling, exercisingon an elliptical machine, exercising on a rowing machine, and crosscountry skiing. Periodic human motions may then be counted that areappropriate to the current user activity. In one embodiment, the systemprovides user data on each of the activities that were performed. Inthis way, a user can monitor his or her overall activity level, as wellindividual activities.

FIG. 1 is a block diagram illustrating an electronic device 100, inaccordance with one embodiment of the present invention. In oneembodiment, the electronic device 100 is a portable electronic devicethat includes one or more inertial sensors. The inertial sensors maymeasure accelerations along a single axis or multiple axes. The inertialsensors may measure linear as well as rotational (angular)accelerations.

The electronic device 100 may be used to identify user activities andcount periodic human motions appropriate to the identified useractivities. In one embodiment, electronic device 100 operates inconjunction with a server (not shown) to identify user activities andcount periodic human motions. In one embodiment, periodic human motionsmay be accurately counted regardless of the placement and/or orientationof the device 100 on a user. For example, the electronic device 100 maybe carried in a backpack, pocket, purse, hand, or elsewhere, andaccurate periodic human motions may still be counted. In one embodiment,the device 100 is placed in one of multiple locations on a human tofacilitate accurate detection of periodic human motions. For example,the electronic device 100 may be placed in a pocket while bicycling toensure the greatest accuracy in counting pedal motions. Periodic humanmotions may be accurately counted whether the electronic device 100maintains a fixed orientation or changes orientation during operation.

The electronic device 100 in one embodiment comprises an accelerationmeasuring logic 105, an activity identification engine 110, a motionprocessor 120, and a motion log 125. The acceleration measuring logic105 may be an inertial sensor or other acceleration sensitiveinstrument. The acceleration measuring logic 105 may continuously takemeasurements of acceleration data. The measurements of acceleration dataare taken at a sampling rate that may be fixed or variable. In oneembodiment, the acceleration measuring logic 105 receives a timingsignal from a timer (not shown) in order to take measurements at thesampling rate. In one embodiment, the acceleration measuring logic 105is coupled to the activity identification engine 110 and to the motionprocessor 120, and acceleration measurement data is sent to the activityidentification engine 110 and to the motion processor 120 forprocessing.

In one embodiment, measurement data is processed by the activityidentification engine 110 to identify a user activity. The activityidentification engine 110 may identify the user activity from aplurality of identifiable user activities. The activity identificationengine 110 may identify a user activity by monitoring for differentevents, each event indicative of a different type of activity. In oneembodiment, when enough events indicative of a particular user activityare detected, the activity identification engine 110 notifies the motionprocessor 120 that the identified activity is being performed by theuser.

The motion processor 120 may process acceleration measurement data todetect periodic human motions. In one embodiment, a series of motioncriteria are applied to the acceleration measurement data. If each ofthe motion criteria are satisfied, a periodic human motion may beidentified, and counted. In one embodiment, a different set of motioncriteria may apply for each user activity. In one embodiment, criteriamay include positive criteria (ones that must be met to classify amotion in a certain way) and negative criteria (ones that cannot be metto classify a motion in a certain way). Once the activity identificationengine 110 has identified a user activity, the motion processor 120 mayapply the set of motion criteria specific to the identified activity todetect appropriate periodic human motions. When an appropriate periodichuman motion is detected, it may be recorded in the motion log 125.

If no user activity has been identified by the activity identificationengine 110, in one embodiment possible periodic human motions arebuffered by the motion processor 120. Once the activity identificationengine 110 identifies the user activity, these buffered possibleperiodic human motions may be recorded in the motion log 125 as periodichuman motions appropriate to the identified user activity.

The motion log 125 may maintain separate data sets for each type ofperiodic human motion. For example, the motion log 125 may have a firstrecord of the number of steps walked in a day and a second record of thenumber of strides a user has inline skated in a day. The motion log 125may also include additional statistics for different periodic humanmotions, for example the average walking speed, the length of time andtime in a day during which a particular activity was performed, etc.

FIG. 2 is a block diagram illustrating a motion identification system200, in accordance with one embodiment of the present invention. Themotion identification system 200 in one embodiment may include anelectronic device. In one embodiment, the electronic device is aportable electronic device that includes one or more inertial sensors.In another embodiment, the motion identification system 200 includes anelectronic device coupled to a server. The distribution of thefunctionality between the portable device and the server may vary. Inone embodiment, the motion identification system 200 comprises a filter220, a cadence logic 232, a rolling average logic 235, a dominant axislogic 227, an activity identification engine 280, a motion processor 230and a motion log 275.

The acceleration measuring logic 205 may be an inertial sensor or otheracceleration sensitive instrument. The acceleration measuring logic 205may continuously take measurements of acceleration data at a samplingrate that may be fixed or variable. In one embodiment, the accelerationmeasuring logic 205 receives a timing signal from a timer (not shown) inorder to take measurements at the sampling rate. In one embodiment, theacceleration measuring logic 205 is a 3-dimensional accelerometer. Inone embodiment, the sampling rate of the acceleration measuring logic205 may be based on a level of activity detected. In one embodiment, forslower motions, the sampling rate is decreased.

In one embodiment, the acceleration measuring logic 205 is coupled tothe filter 220. Measurement data may be processed by the filter 220 toremove noise. The filter 220 may be implemented in hardware, software,or both hardware and software. In one embodiment, the filter 220includes multiple filters, and a determination of which filters to applyto the measurement data is made based upon the type of user activitydetected. For example, a low pass filter may be used to remove highfrequency noise that would interfere with step counting when a user iswalking. In contrast, a high pass filter may be used when quick motionsare to be monitored.

Many types of motions that are useful to keep track of have a periodicset of movements. Specific periodic human motions may be characteristicof different types of user activity. For example, to walk, an individualmust lift a first leg, move it forward, plant it, then repeat the sameseries of motions with a second leg. In contrast, a person inlineskating performs a repeated sequence of pushing, coasting and liftofffor each leg. For a particular individual, the series of walking motionswill usually occur in about the same amount of time, and the series ofskating motions will usually occur in about the same amount of time. Therepeated set of motions can be considered a unit, and defines the motioncycle. The amount of time that it takes to complete one motion cycledefines the motion cycle's period, and the number of motion cycles thatoccur in a given unit of time define the motion cycle's cadence.

In one embodiment, filtered measurement data may be passed on to thecadence logic 232. The cadence logic 232 may determine a period and/orcadence of a motion cycle. The period and/or cadence of the motion cycleis generally based upon user activity (e.g. inline skating, biking,running, walking, etc). In one embodiment, the motion cycle's period isupdated after each periodic human motion. The current motion cycleperiod may be a rolling average of the motion cycle periods over theprevious set of motions of the same activity type. This rolling averagedata may be received from the rolling average logic 235, as discussed inmore detail below.

In one embodiment, the cadence logic 232 maintains one or more cadencewindows. A cadence window is a window of time since a last periodichuman motion was counted that is looked at to detect a new periodichuman motion. When motion criteria (e.g., threshold conditions) are metwithin a cadence window, a periodic human motion is detected, whereaswhen motion criteria are met outside of the cadence windows no periodichuman motion is detected. A cadence window may be used to facilitateaccurate measurement of a periodic human motion (e.g., a step, inlineskating stride, bicycle pedal, rowing stroke, etc.).

A cadence window may be set based on the period and/or cadence of theactual motion cycle (e.g., a stepping period), on set limits, and/or onother determiners. In one embodiment, the cadence window is determinedby measuring lengths of time since the most recent periodic human motionwas counted. In one embodiment, the cadence window is a dynamic cadencewindow that continuously updates as a user's cadence changes during aparticular activity. For example, using a dynamic cadence window, a newcadence window length may be set after each periodic human motion. If noprevious periodic human motions have been detected, or if fewer than aset number of periodic human motions to determine a dynamic cadencewindow have been detected, a default cadence window may be used. Onceenough periodic human motions have been detected to determine a dynamicmotion cycle cadence or period, the cadence window may be set to thedetermined motion cycle period plus or minus an error factor. In oneembodiment, a count of between about two to about ten periodic humanmotions is sufficient to set a dynamic cadence window.

In one embodiment, a separate cadence window may be maintained for eachidentifiable user activity. Each identifiable user activity may have,for example, a unique default cadence window. When no specific currentuser activity has yet been identified, these separate cadence windowsmay be maintained concurrently for each of the possible user activities.Periodic human motions that occur outside of a default cadence windowmay indicate that the associated user activity is currently not beingperformed.

Once the motion cycle's period is detected, the cadence logic 232 mayset one or more sample periods for the rolling average logic 235 to usebased upon the motion cycle's period. A sample period can include aspecified time frame over which obtained measurements are used toperform a calculation. In one embodiment, the sample period(s) are setsuch that at least one sample period is approximately the length of, orlonger than, the motion cycle's period. In one embodiment, a sampleperiod is set such that it is a multiple of the motion cycle's period.

In one embodiment, the rolling average logic 235 may receive filteredmeasurement data from the filter 220 and one or more sample periods fromthe cadence logic 232. The rolling average logic 235 creates one or morerolling averages of accelerations as measured by the inertial sensor(s)over the sample period(s) set by the cadence logic 232. The rollingaverages of accelerations may be used for determining an orientation ofthe electronic device, for determining thresholds to compareacceleration measurements against, and/or for other purposes. In oneembodiment, the rolling average logic 235 creates one or more rollingaverages of accelerations for determining an orientation of theelectronic device 200, at least one rolling average having a period thatis at least the motion cycle's period. In one embodiment, the rollingaverage logic 235 creates a rolling average of accelerations fordetermining a lower threshold to compare acceleration measurementsagainst, the rolling average having a sample period that is at leasttwice the stepping period.

The rolling average logic 235 may create one or more rolling averages ofdata other than accelerations. In one embodiment, the rolling averagelogic 235 creates a rolling average of motion cycle periods, where therolling average is the rolling average time between periodic humanmotions. In one embodiment, the rolling average of motion cycle periodsis calculated over the past four counted periodic human motions. Therolling average of the motion cycle periods may be used by the cadencelogic 232 to dynamically determine a cadence window and a current motioncycle cadence.

In one embodiment, rolling averages may be maintained in registries thatkeep track of rolling average values and the number of samples that wereused to calculate current rolling average values. When a new measurementis taken, it can be incorporated into the rolling average value, and theregistry can than be updated with a new rolling average value.Alternatively, the rolling averages may be maintained by buffering themeasurements used to calculate the rolling averages. As the buffersfill, oldest measurement data can be discarded and replaced by newmeasurement data. The measurements in the buffer can be averaged aftereach measurement to determine a new rolling average.

In one embodiment, the dominant axis logic 227 receives at leastfiltered acceleration data from the filter 220, and rolling averages ofaccelerations from the rolling average logic 235. In one embodiment, thedominant axis logic 227 receives only rolling averages of accelerationsfrom the rolling average logic 235. The dominant axis logic 227 mayassign a dominant axis based on the received information.

In one embodiment, the dominant axis logic 227 determines an orientationof the electronic device and/or the inertial sensor(s) within the motionidentification system 200. The orientation may be determined based uponthe rolling averages of accelerations created by the rolling averagelogic 235. In one embodiment, once the orientation is determined, adominant axis is assigned based upon the orientation. Determining anorientation of the electronic device may include identifying agravitational influence. The axis with the largest absolute rollingaverage may be the axis most influenced by gravity, which may changeover time (e.g. as the electronic device is rotated). Therefore, a newdominant axis may be assigned when the orientation of the electronicdevice and/or the inertial sensor(s) attached to or embedded in theelectronic device changes.

In one embodiment, the axis with the largest absolute rolling averageover the sample period is assigned as the dominant axis. In oneembodiment, the dominant axis does not correspond to one of the actualaxes of the inertial sensor(s) in a current orientation, but rather toan axis that is defined as approximately aligned to gravity. In oneembodiment, the dominant axis corresponds to a virtual axis that is acomponent of a virtual coordinate system. In one embodiment, thedominant axis setting logic 240 assigns the dominant axis by performinga true gravity assessment, such as by doing trigonometric calculationson the actual axes based on the gravitational influence. In oneembodiment, the dominant axis setting logic 240 assigns the dominantaxis by comparing the gravitational influence to a data structure suchas a lookup table, associative array, hash table, adjacency matrix, etc.

In one embodiment, the dominant axis is determined based on criteriaother than a device orientation in relation to gravity. For example, thedominant axis may be determined based on an axis that has a greatestpeak to peak variation within a motion cycle. In one embodiment,multiple dominant axes are assigned, each dominant axis being associatedwith a different user activity. Each of the dominant axes may beassigned based on criteria specific to an associated user activity. Forexample, a first dominant axis may be assigned to the user activity ofwalking based on an axis that is most influenced by gravity, and asecond dominant axis may be assigned to the user activity of bicyclingbased on an axis that has the greatest peak to peak variance.

The activity identification engine 280 may receive as an input filteredacceleration measurement data from the filter 220. The activityidentification engine 280 may also receive a cadence and/or period of acurrent motion cycle from the cadence logic 232, a current cadencewindow from the cadence logic 232, rolling averages (e.g., ofaccelerations) from the rolling average logic 235, and one or moredominant axes from the dominant axis logic 227.

In one embodiment, the activity identification engine 280 includesmultiple activity identification logics 285. In one embodiment, each ofthe activity identification logics 285 is set up to determine whetherthe current acceleration data defines a particular activity.Alternatively, activity identification logic 285 may be capable ofidentifying two or more different activities. In one embodiment, theactivity identification engine 280 includes only a single activityidentification logic 285, which is used to identify all identifiableuser activities. Alternatively, there may be separate activityidentification logics 285 for each activity, there may be separateactivity identification logics 285 for each activity type, or there maybe separate activity identification logics 285 based on some othercriteria.

An activity identification logic 285 identifies a specific user activityby monitoring for acceleration data indicative of that type of activity.The acceleration data is analyzed and the activity identification logic285 may make its determination based on one or more of the receivedacceleration data, motion cycle cadence, motion cycle period, cadencewindows, rolling averages, and dominant axes. In one embodiment, whenenough events indicative of a particular user activity are detected byan activity identification logic 285, the activity identification engine280 notifies the motion processor 230 that the identified activity isbeing performed by the user. In one embodiment, each time an activityidentification logic 285 detects an event indicative of a particulartype of activity, that event is forwarded to the motion processor 230.The motion processor 230 may then determine whether the user isperforming the activity associated with the received event or receivedevents. In one embodiment, the activity identification logics 285 alsomonitor for negative events that indicate that a user is not performinga particular user activity. These negative events may also be used bythe activity identification engine 280 and/or the motion processor 230to identify a current user activity.

FIGS. 3A and 3B illustrate examples of motion data which may be used byan activity identification logic that identifies bicycling by monitoringfor positive and negative bicycling events, in accordance withembodiments of the present invention. FIG. 3A illustrates a firstexemplary motion cycle graph 300 that shows a user engaged in a firstuser activity as analyzed by an activity identification logic thatidentifies bicycling, in accordance with one embodiment of the presentinvention. The exemplary motion-cycle graph 300 shows acceleration datataken with a single tri-axis inertial senor. The acceleration at a givenperiod of time is represented for a first axis 303, a second axis 305,and a third axis 307.

The bicycling identification logic monitors for positive events andnegative events that occur when certain motion criteria are satisfied.In one embodiment, the bicycling identification logic uses a dominantaxis that is defined by measuring the axis with the largest peak to peakdifference. In the illustrated figure, the first axis 303 is thedominant axis, because it has the greatest peak to peak difference. Inone embodiment, the bicycling identification logic only examinesacceleration data along the dominant axis.

In one embodiment, the bicycling identification logic uses a rollingaverage of accelerations 310 for the dominant axis to set a firstthreshold 315 and a second threshold 320. The first threshold 315 andsecond threshold 320 may be set based on offsets from the rollingaverage of accelerations 310. Alternatively, the first threshold 315 andsecond threshold 320 may be set based on a percentage of the peak topeak difference, on a percentage of the average of the peak to peakdifference, on a percentage of half of the peak to peak difference, oron other criteria. For example, the first threshold 315 may be set at25% of the peak to peak difference, and the second threshold 320 may beset at 75% of the peak to peak difference.

A first periodic human motion may be detected when the first threshold315 is first crossed 325 (moving away from zero). This may trigger apositive bicycling event. A second periodic human motion may be detectedwhen the first threshold 315 is crossed a second time 330 (moving awayfrom zero). In one embodiment, if the first threshold 315 is crossed asecond time 330 while within a cadence window 345, a positive bicyclingevent may be triggered. If the first threshold 315 is not crossed 330while within the cadence window 345, a negative bicycling event may betriggered. The cadence window 345 has a cadence window minimum 343 thatopens the cadence window 345 some period of time after the firstthreshold 315 is first crossed 325, and a cadence window maximum 347that closes the cadence window 345 some period of time thereafter. Inthe illustrated example, the first threshold 315 is crossed within thecadence window 345, triggering a positive bicycling event.

The second threshold 320 may be used by the bicycling identificationlogic to determine a peak shape for the acceleration measurements alongthe first axis 303. In one embodiment, the bicycling identificationlogic notes each time the second threshold 320 is crossed. In oneembodiment, if the time between when the second threshold 320 is firstcrossed 335 and second crossed 340 is less than a peak window 350, thena negative bicycling event may be triggered. If the time between whenthe second threshold 320 is first crossed 335 and second crossed 340 isgreater than or equal to the peak window 350, then a negative bicyclingevent may not be triggered. In one embodiment, if the second threshold320 is crossed within 25-35% or 65-75% of the average of the motioncycle's period, the peak is too steep, and a negative event istriggered. In one embodiment, if the second threshold 320 is crossedwithin certain percentages of the average of the motion cycle's cadence,a negative event is triggered. In the illustrated example, no negativebicycling events are triggered. Moreover, the illustrated example showsan embodiment in which a sufficient number of positive events aretriggered to identify the current user activity as bicycling.

FIG. 3B illustrates a second exemplary motion cycle graph 350 that showsa user engaged in a second user activity as analyzed by an activityidentification logic that identifies bicycling, in accordance with oneembodiment of the present invention. The exemplary motion-cycle graph350 shows acceleration data taken with a single tri-axis inertial senor.The acceleration at a given period of time is represented for a firstaxis 353, a second axis 355, and a third axis 357.

The bicycling identification logic monitors for positive events andnegative events that occur when certain motion criteria are satisfied.In one embodiment, the bicycling identification logic uses a dominantaxis that is defined by measuring the axis with the largest peak to peakdifference. In the illustrated figure, the second axis 355 is thedominant axis, because it has the greatest peak to peak difference.

In one embodiment, the bicycling identification logic uses a rollingaverage of accelerations 360 for the dominant axis to set a firstthreshold 365 and a second threshold 370. A first periodic human motionmay be detected when the first threshold 365 is first crossed 375(moving away from zero). This may trigger a positive bicycling event. Asecond periodic human motion may be detected when the first threshold365 is crossed a second time 380 (moving away from zero). In oneembodiment, if the first threshold 320 is crossed a second time 380while within a cadence window 398, a positive bicycling event may betriggered. If the first threshold 365 is not crossed 380 while withinthe cadence window 398, a negative bicycling event may be triggered. Inthe illustrated example, the first threshold 365 is crossed within thecadence window 398, triggering a positive bicycling event.

The second threshold 370 may be used by the bicycling identificationlogic to determine a peak shape for the acceleration measurements alongthe second axis 355. In one embodiment, the bicycling identificationlogic notes each time the second threshold 370 is crossed. The timebetween when the second threshold 370 is first crossed 385 and secondcrossed 388 is less than a peak window 390, triggering a negativebicycling event. The negative bicycling event may reset a counter thatcounts a number of positive bicycling events. Each illustrated peakwould trigger a negative bicycling event. Therefore, not enough positivebicycling events are triggered to identify the current user activity asbicycling.

Returning to FIG. 2, the motion processor 230 may receive positive andnegative events from the activity identification logics 285 of theactivity identification engine 280. The motion processor 230 may alsoreceive dominant axis data from the dominant axis logic 227, rollingaverage data from the rolling average logic 235, a cadence and periodfor a current motion cycle from the cadence logic 232, cadence windowsfrom the cadence logic 232, and filtered acceleration data from thefilter 220. The motion processor 230 may include a counting logic 260,mode logic 255, and one or more count buffers 265.

In one embodiment, the mode logic 255 receives positive and negativeevents from the activity identification logics 285. The mode logic 255may then determine an appropriate mode for the counting logic 260 basedon the received events. The mode logic 255 may also determine anappropriate mode based on the count buffers 265. For example, in oneembodiment, while in an entry mode, the first count buffer to reach apredetermined value determines which active mode to initiate. In oneembodiment, the mode logic 255 includes a plurality of active modes,each active mode corresponding to a different activity. In oneembodiment, the mode logic 255 includes an entry mode, an exit mode anda sleep mode. Operating modes are discussed in greater detail below inreference to FIG. 5.

The counting logic 260 may receive data from the dominant axis logic227, rolling average logic 235, cadence logic 232 and filter 220. Thecounting logic 260 may use this data to count periodic human motions. Inone embodiment, the behavior of the counting logic 260 is determined byits current operating mode.

The counting logic 260 may determine which measurements to use todetermine if a periodic human motion has occurred. In one embodiment,the counting logic 260 may monitor accelerations relative to thedominant axis, and select only those measurements with specificrelations to the dominant axis for measurement. For example, onlyaccelerations that are approximately parallel to the dominant axis maybe selected (e.g., in walking active mode), or alternatively, onlyaccelerations that are approximately perpendicular to the dominant axismay be selected (e.g., in rowing active mode). In one embodiment, thecounting logic 260 selects only measurements of acceleration data alongthe dominant axis. In alternative embodiments, measurements ofacceleration data along other axes may also be used.

Selected measurements may be compared to motion criteria to determinewhether a periodic human motion has occurred, and to determine what typeof periodic human motion has occurred. Examples of motion criteriainclude comparisons of current measurements to previous measurements,comparisons of current measurements to threshold values, slope analysis,curve fitting analysis, peak analysis, etc. In one embodiment, the samecriteria that are used to identify events by the activity identificationlogics are also used to count periodic human motions. Alternatively,different criteria may be used. In one embodiment, the motion criteriaused are dependent on a current operating mode of the counting logic260.

The count buffers 265 keep track of a probable count of periodic humanmotions. In one embodiment, the motion processor 230 includes aplurality of count buffers 265, each associated with a different useractivity. Alternatively, a single count buffer may be used to bufferperiodic human motions for two or more user activities. In oneembodiment, a single count buffer 265 maintains buffers for all useractivities.

The exact behavior of the count buffers 265 may depend on the operatingmode of the counting logic 260. For example, in entry mode, a separatecount buffer may be maintained for each possible user activity. When thesystem identifies a periodic human motion that meets the criteria of aparticular user activity, the count buffer corresponding to that useractivity may be incremented. A detected periodic human motion may meetthe criteria for a number of different user activities. Until a currentuser activity is identified, in one embodiment such motion would becounted by each of the corresponding count buffers 265.

Depending on the current mode, when one of the count buffers 265 reachesa certain number, that count buffer may be emptied, and the motion log275 updated by the count from the count buffer. This may also cause themotion logic 255 to place the counting logic 260 in an active modeappropriate to the count buffer that was emptied. The number of periodichuman motions that must be counted by the a count buffer before they aremoved to the motion log 275 may vary from one to ten or more, dependingon the current operating mode. Moreover, count buffers corresponding todifferent user activities, may have different count requirements.

The motion log 275 keeps track of the total number of periodic humanmotions that have occurred, and the associated activities. In oneembodiment, this data is transmitted to a server or remote database. Theoutput of the motion log 275, in one embodiment, is made available tothe user. In one embodiment, the user can view the various activities,and associated data through a web page.

FIG. 4A illustrates a flow diagram for a method 400 of monitoring humanactivity using an inertial sensor, in accordance with one embodiment ofthe present invention. The method may be performed by processing logicthat may comprise hardware (e.g., circuitry, dedicated logic,programmable logic, microcode, etc.), software (such as instructions runon a processing device), or a combination thereof. In one embodiment,method 400 is performed by the electronic device 100 of FIG. 1. In oneembodiment, method 400 is performed by the motion identification system200 of FIG. 2.

Referring to FIG. 4A, method 400 begins with monitoring accelerations(block 405). Accelerations may be monitored with an inertial sensor, orother acceleration monitoring device. The monitored acceleration mayinclude filtered and processed acceleration data, as described above. Atblock 410, the motion is identified as corresponding to one of aplurality of user activities based on the accelerations. At block 415,periodic human motions are counted appropriate to the identifiedactivity. Examples of identifiable user activities include, walking,running, inline skating, rowing, exercising on an elliptical machine,and bicycling. Other user activities may also be identified.

Periodic human motions corresponding to multiple different predetermineduser activities may be identified in embodiments of the presentinvention. Moreover, in one embodiment, new user activities may be addedto the list of recognized periodic human motions. To add such periodichuman motions to the predetermined user activities, a user may record anew user activity.

FIG. 4B illustrates a flow diagram for a method 450 of recording amotion profile, in accordance with one embodiment of the presentinvention. Method 450 may be used to record new user activities, or tocalibrate existing user activities to a particular user. Calibration ofexisting user activities may improve counting accuracy. The method 450may be performed by processing logic that may comprise hardware (e.g.,circuitry, dedicated logic, programmable logic, microcode, etc.),software (such as instructions run on a processing device), or acombination thereof. Method 450 may be performed by the electronicdevice 100 of FIG. 1, and by the motion identification system 200 ofFIG. 2.

Referring to FIG. 4B, the method 450 begins by receiving a command torecord a motion profile (block 455). At block 460, input is receivedthat indicates the user activity for which the motion profile will berecorded. In one embodiment, the system may enable the user to selectone of a displayed list of existing user activities. In one embodiment,the user may select one of the displayed user activities or may chooseto identify a new user activity to be associated with the motionprofile. If a motion profile is to be recorded for a new user activity,the user may be prompted to enter a name for the new user activity.

At block 465, a command is received to begin recording. At block 470,measurements of accelerations are recorded. The measurements ofaccelerations are recorded while a user performs the user activity forwhich a motion profile is to be recorded.

At block 475, the process determines whether the recorded measurementsdefine a unique pattern. In one embodiment, measurements ofaccelerations are recorded until a unique pattern can be determined. Aunique pattern differentiates the recorded pattern from other motionprofiles.

If a unique pattern has not yet been identified, the process determinesif the user has stopped the recording and/or motion, at block 480. Inone embodiment, if the user stops recording before such a unique patternis identified, the system prompts the user to continue recording, atblock 485. The process then returns to block 470 to continue recordingmotion data. If the user has not stopped the recording, the processcontinues directly to block 470. The time to determine a pattern mayrange from recording a single motion cycle, to recording many motioncycles, which may occur over a single second to minutes. In oneembodiment, user input may be received to specify the period of acurrent motion cycle. For example, a user may push a button, make acertain movement, make a loud noise, etc. each time a new periodic humanmotion ends (e.g., each time a step is completed). This may reduce thenumber of motion cycles that need to be recorded in order to determine apattern from the recorded measurements. Alternatively, the period of themotion cycle is determined without user input. In one embodiment, thesystem utilizes at least two full motion cycles to identify the pattern.

If at block 475, a unique pattern has been identified, the processcontinues to block 490. In one embodiment, the user is notified that thenew activity pattern has been successfully identified. At block 490, inon embodiment a motion cycle average may be determined for the activity.Once a unique pattern of accelerations is determined for the activity,at block 495, the motion profile may be added to either an existing useractivity or to a new user activity.

The unique pattern of accelerations may also be used to determine (set)positive and/or negative events for the activity. For example, if theunique pattern of accelerations shows a repeated sharp acceleration peakalong a dominant axis, a sharp acceleration peak along the dominant axismay be added as a new positive event and/or a gradual peak along thedominant axis may be added as a new negative event. If method 450 wasinitiated to calibrate an existing user profile, the calibration mayinclude adding new positive and/or negative events to the existingpositive and/or negative events. If method 450 was initiated to record anew activity, new positive and/or negative events may be assigned tofacilitate future identification of the new activity.

FIG. 5 shows a state diagram for the behavior 500 of a system formonitoring human activity, in accordance with one embodiment of thepresent invention. The system may have multiple operating modes (states)that are navigated between by processing logic that may comprisehardware (e.g., circuitry, dedicated logic, programmable logic,microcode, etc.), software (such as instructions run on a processingdevice), or a combination thereof. In one embodiment, behavior 500 isthe behavior of the electronic device 100 of FIG. 1. In one embodiment,behavior 500 is the behavior of the electronic device or inertialsensor(s) within motion identification system 200 of FIG. 2.

The behavior 500 may include multiple operating modes for monitoringhuman activity: a sleep mode, an entry mode, one or more active modes,and an exit mode. In alternative embodiments, more or fewer modes may beused. In one embodiment, only two modes are used: active mode andnon-active mode. The active mode is entered once periodic human motionsare detected, while the non-active mode is used for all other states. Inalternative embodiments, multiple inactive modes and/or active modes areused. To navigate between modes, certain conditions must be met. Theconditions may include exit conditions for terminating an active modeand entry conditions for initiating inactive modes. Each mode has one ormore exit and entry conditions.

Use of different conditions for different operating modes increases thereliability of the device that is monitoring the human activity. Forexample, once an object (e.g., a person) is moving, they are more likelyto remain moving than to stop. Likewise, if a person is not moving, theyare more likely not to move than to begin moving. Moreover, if a personis running, he is more likely to continue running than to beginbicycling. These principles can be applied by requiring more stringentconditions to be met for a device to initiate an active mode than tocontinue the active mode. The different modes may each have rules thatreflect what is more likely to happen for subsequent measurements. Thismay reduce or eliminate the number of uncounted periodic human motionsand/or false periodic human motion counts.

Referring to FIG. 5, operating modes in one embodiment include a sleepmode 505, an entry mode 515, an active mode 525, and an exit mode 535.In one embodiment, the power level of the system or device is linked tothese modes.

The first mode initiated is the sleep mode 505. When no activity(acceleration) is detected, the system remains in sleep mode 505. Whenacceleration is detected, an entry mode 515 is initiated. In oneembodiment, in sleep mode 505 the inertial sensor has a slow samplerate, thereby reducing power consumption of the system.

Once in entry mode 515, in one embodiment the sampling rate of theinertial sensor is increased to ensure that acceleration is monitored todetect periodic human motions. In entry mode 515, periodic human motionsappropriate to all identifiable user activities may be monitored.Therefore, in one embodiment, the sampling rate is sufficiently high toproperly detect the fastest possible activity. When entry conditions aresatisfied for a particular user activity, an appropriate active mode 525is initiated. The sampling rate is then adjusted to be appropriate forthe activity being detected, in one embodiment. If no entry conditionsfor any of the identifiable user activities are detected in a period oftime, sleep mode 505 is reinitiated. In one embodiment, sleep mode 505is only initiated if no motion is detected.

Once in the appropriate active mode 525, acceleration data is monitoredto count periodic human motions according to a predefined set of rulesor motion criteria. According to one of these criteria, periodic humanmotions are expected to occur within a set interval (e.g., within acadence window). When a periodic human motion is counted within the setinterval, then the current active mode 525 is continued. When a periodichuman motion is not detected within the set interval, an expectedperiodic human motion has not occurred, and an exit mode 535 isinitiated.

In exit mode 535, processing logic determines whether re-entryconditions are met. When the re-entry conditions are met, theappropriate active mode 525 is reinitiated. If the re-entry conditionsare not met within a certain time frame, the process initiates entrymode 515 again. From entry mode 515, any appropriate active mode 525 mayagain be entered into if certain entry conditions are satisfied, or theprocess may return to sleep mode 505.

FIG. 6 illustrates a flow diagram for a method 600 of operating anelectronic device in sleep mode, in accordance with one embodiment ofthe present invention. In one embodiment, method 600 corresponds to thesleep mode 505 of FIG. 5. In one embodiment, the method 600 may beginwhen no relevant acceleration has been detected for a predetermined timeinterval, or when no periodic human motions have been detected for apredetermined time interval. In one embodiment, when no accelerationabove a threshold value is detected for a set period of time, the sleepfunction is initiated. In another embodiment, when a motion signatureindicative of an activity that does not need to be monitored isdetected, the sleep function is initiated. For example, when the motionsignature of driving is detected, the sleep function may be initiated.The time period that elapses before the sleep mode is initiated may be afixed value, or it may be adjusted automatically by processing logic orbased on user input (e.g. in response to a user selection of desiredbattery longevity versus desired performance, or based on the lastmeasured cadence window).

Referring to FIG. 6, method 600 begins with setting a sleep modesampling rate (block 605). In one embodiment, a low sampling rate isset. This reduces power consumption and prolongs battery life. In oneembodiment, the sleep mode sampling rate is a fixed value. Inalternative embodiments, the sleep mode sampling rate can be modifiedautomatically by processing logic based on certain criteria such as timeof day, user behavior patterns, etc., or based on user input.

In one embodiment, a sampling function is periodically executed in sleepmode, wherein the sampling function samples acceleration data at a setsampling rate for a set time period. For example, the sampling functionmay be executed every ten seconds for the duration of one second, and asampling rate of fifty measurements per second may be set for that onesecond of operation. In one embodiment, the sampling function repeats ata relatively slow rate (e.g., once every 10 seconds), and the samplingrate within the sampling function is relatively high (e.g., 50 Hz). Thesampling function may be used to detect unwanted motion signatures, orto maintain a device in low power sleep mode, for example, while a useris driving in a car.

In one embodiment, the sleep mode sampling rate is set to zero. Thesleep mode sampling rate may be set to zero, for example, when aninertial sensor has ‘inertial wakeup’ functionality. Inertial wakeupfunctionality enables processing logic to switch from sleep mode toentry mode when an acceleration exceeding a set threshold is detected.The inertial wakeup may be used to simultaneously exit sleep mode andpower-up additional functionality.

At block 610, measurements of acceleration data are taken. At block 615,processing logic determines whether or not relevant acceleration isdetected. Relevant acceleration includes acceleration that meets certaincriteria. In one embodiment, the criteria include a lower threshold andan upper threshold. In alternative embodiments, other criteria may alsobe used, such as a requirement that acceleration be continuouslymeasured for a preset time period.

When no relevant acceleration is detected, or when the ‘inertial wakeup’pin has not triggered (for inertial sensors having ‘inertial wakeupfunctionality’), sleep mode continues, and further measurements ofacceleration data are taken at the set sleep mode sampling rate (block610). When acceleration is detected, sleep mode is terminated and entrymode is initiated (block 620). In one embodiment, the acceleration thatis detected and its rate of change must meet certain criteria toterminate sleep mode.

FIG. 7A illustrates a flow diagram for a method 700 of operating anelectronic device in entry mode, in accordance with one embodiment ofthe present invention. In one embodiment, method 700 corresponds to theentry mode 515 of FIG. 5. The entry mode may be initiated when a userfirst begins an activity in which periodic human motions may bedetected. In one embodiment, the method 700 begins when any relevantacceleration is detected. In one embodiment, entry mode is initiatedwhen a measurement of acceleration that meets certain criteria has beendetected. In one embodiment, method 700 is initiated when a sleep modeis terminated. In one embodiment, method 700 is performed concurrentlyby each of the activity identification logics 285 of FIG. 2 when themotion identification system 200 and/or the counting logic 280 of theelectronic device are in entry mode.

Referring to FIG. 7A, method 700 begins by setting the sampling rate toan active sampling rate (block 704). The active sampling rate is set tofacilitate accurate measurements of periodic human motions, and may be afixed or a dynamically variable rate. A variable sampling rate mayautomatically adjust depending on a period of a detected motion cycle'scadence, may be user adjusted, may adjust based on applications beingrun by processing logic, or by other means. The active sampling rate maybe set to anywhere between about 10 and about 200 Hz. In one embodiment,the active sampling rate is set to about 15 to 40 Hz.

At block 710, a first periodic human motion is recognized. Since noprevious periodic human motions have been measured, and there is nocadence window, the first periodic human motion may be recognized at anytime. Once a first periodic human motion is recognized, a defaultcadence window is set (block 714). The default cadence window may have aminimum and maximum such that periodic human motions will be counted formost or all possible motion cycle cadences. A different default cadencewindow may be set for each activity. In one embodiment, a separateinstance of method 700 is run for each instance of an activityidentification logic.

In one embodiment, an initial default value for the cadence window isset wide enough to accommodate all users. In one embodiment, the cadencewindow is then dynamically adjusted to match the specific user.Processing logic may ‘learn’ (adapt to) a particular user, and maybecome more accurate as periodic human motions are counted. Processinglogic that has the ability to learn or adapt to different users maycreate an individualized profile for each user. Multiple profiles mayalso be created for each user, the different profiles reflectingdifferent user activities. For example, a first profile might be createdfor a user's running and a second profile may be created for a user'swalking. Processing logic may switch between different profilesautomatically, or manually based on user input. In one embodiment,processing logic compares a current cadence and/or motion cycle patternto stored profiles. When a current cadence or motion cycle patternmatches that of a stored profile, that profile may be activated.

At block 720, a buffered count is set to one. At block 724, processinglogic determines whether an additional periodic human motion isrecognized. An additional periodic human motion may be recognized if aparticular measurement of acceleration meets all the necessary criteria.Different criteria may be necessary for each of the different useractivities. One embodiment of these criteria is discussed below withreference to FIG. 10.

Returning to FIG. 7A, if an additional periodic human motion isrecognized, method 700 continues to block 744. If no additional periodichuman motions are recognized, then processing logic determines whetherthe time is still within the cadence window (block 730). If there isstill time within the cadence window, the process returns to block 724.If the cadence window has closed, then the buffered count is reset tozero (block 734). The process then continues to block 740.

At block 740, processing logic determines whether any relevantacceleration is detected. If no relevant acceleration is detected, thensleep mode is initiated (block 742). If some relevant acceleration isdetected, then processing logic returns to block 710 to awaitrecognition of another periodic human motion. If at block 740 anadditional periodic human motion was recognized, the process continuesto block 744.

At block 744, an additional periodic human motion is added to thebuffered count. Processing logic then checks whether any negative eventshave been recognized (745). A negative event is an indicator that themotion does not belong to the activity category being tested. If anegative event is recognized, then the process continues to block 734,and the buffered count is reset to zero. If a negative event is notrecognized, then the process continues to block 746.

At block 746 the process determines whether another user activityassociated with a different instance of method 700 has been identified.If another user activity has been identified, then the process ends. Ifno other user activity has been identified yet, then the processcontinues to block 748.

At block 748, processing logic checks whether there are N periodic humanmotions in the buffered count. When there are not yet N periodic humanmotions in the buffered count, the process returns to block 724 tocontinue in entry mode. When the number of periodic human motions in thebuffered count reaches N, the buffered periodic human motions are addedto a motion log, and an appropriate active mode is entered into (block749).

FIG. 7B illustrates a flow diagram for a method 750 of operating anelectronic device in entry mode, in accordance with another embodimentof the present invention. In one embodiment, method 750 corresponds tothe entry mode 515 of FIG. 5. The entry mode may be initiated when auser first begins an activity in which periodic human motions may bedetected. In one embodiment, the method 750 begins when any relevantacceleration is detected. In one embodiment, entry mode is initiatedwhen a measurement of acceleration that meets certain criteria has beendetected. In one embodiment, method 750 is initiated when a sleep modeis terminated.

Referring to FIG. 7B, method 750 begins by setting the sampling rate toan active sampling rate (block 754). The sampling rate, in oneembodiment, depends on the highest sampling rate required by apotentially recognizable activity.

At block 757, a first periodic human motion is recognized for eachidentifiable user activity. Since no previous periodic human motionshave been measured, and there is no cadence window, the first periodichuman motion may be recognized at any time. Once a first periodic humanmotion is recognized, default cadence windows are set (block 760).Default cadence windows may be set for each type of identifiable useractivity.

At block 763, a buffered count is set to one for each of theidentifiable user activities. At block 765, processing logic determineswhether additional periodic human motions are recognized for each of theidentifiable user activities. Different criteria may be used toseparately determine whether an additional periodic human motion isrecognized for each of the user activities.

For user activities for which an additional periodic human motion isrecognized, method 750 continues to block 783. Concurrently, for useractivities for which no additional periodic human motion is recognized,the process continues to block 770. At block 770, the process determineswhether the time is still within the appropriate cadence window for eachappropriate user activity. If there is still time within the appropriatecadence window, the process returns to block 765 for that user activity.If the cadence window has closed, then the appropriate buffered count isreset to zero (block 775). The process then continues to block 777.

At block 777, processing logic determines whether any relevantacceleration is detected. If no relevant acceleration is detected forany of the user activities, then sleep mode is initiated (block 779). Ifsome relevant acceleration is detected for any user activity, thenprocessing logic returns to block 757 to await recognition of anotherfirst periodic human motion for the appropriate user activity.

If at block 765 an additional periodic human motion was recognized, theprocess continues to block 783 for the appropriate user activity, and anappropriate periodic human motion is added to an appropriate buffercount.

At block 788, processing logic checks whether any negative events havebeen recognized for each appropriate user activity. For user activitiesfor which a negative event is recognized, the process continues to block775, and the appropriate buffered count is reset to zero. For useractivities for which no negative event is recognized, the processcontinues to block 790.

At block 790 the process determines whether sufficient periodic humanmotions have been recognized to identify a current user activity. Ifsufficient periodic human motions have been recognized for a particularuser activity, the process continues to block 795. At block 795, theentries in the appropriate buffered count are added to the motion log,and an active mode is initiated for the identified activity.

FIG. 8 illustrates a flow diagram for a method 800 of operating anelectronic device in an active mode, in accordance with one embodimentof the invention. In one embodiment, method 800 corresponds to theappropriate active mode 525 of FIG. 5. The active mode may be initiatedwhen a particular user activity has been identified. In one embodiment,method 800 is initiated when an entry mode is terminated, and/or when anexit mode is terminated.

Referring to FIG. 8, method 800 begins by setting a cadence window(block 810). The cadence window may be set based on previous measurementdata. In one embodiment, the cadence window is set based on a rollingaverage of motion cycle periods. In one embodiment, the cadence windowmay be identical to an appropriate cadence window used during entrymode. Once the cadence window is set, measurement data is checked todetermine whether an additional periodic human motion is recognized(block 815). If an additional periodic human motion is recognized, thenit is added to the motion log (block 820). If no additional periodichuman motion is recognized, then the process determines whether anegative event has been recognized 822. If a negative event has beenrecognized, the process continues to block 830, and exit mode isinitiated. If no negative event has been recognized, the processcontinues to block 825.

At block 825, the process determines whether the current measurement wastaken within the cadence window. If the cadence window has not elapsed,the process returns to block 815. If the cadence window has elapsed,then an expected periodic human motion was not counted, and an exit modeis initiated (block 830).

FIG. 9 illustrates a flow diagram for a method 900 of operating anelectronic device in exit mode, in accordance with one embodiment of thepresent invention. In one embodiment, method 900 corresponds to the exitmode 535 of FIG. 5. The exit mode may be entered into when an expectedperiodic human motion is not identified in an active mode.

In one embodiment, the requirement(s) for changing from exit mode to anactive mode are less strict than the requirement(s) for switching fromentry mode to an active mode. Processing logic may assume that when auser has recently performed a periodic human motion, the user is mostlikely repeat the same periodic human motion. For example, a user who isinline skating is much more likely to continue skating movements than tobreak into a jog. Processing logic may also assume that if a user iscurrently inactive, it is most likely that the user will remaininactive. These assumptions may be implemented by imposing morestringent requirements to switch from entry mode to an active mode thanto change from exit mode to an active mode. The requirements to remainin an active mode may be even less stringent than the requirements toinitiate the active mode, whether from the entry mode or from the exitmode.

An expected periodic human motion may not be identified, for example,when a user stops moving, when extraneous movements such as gestures aremade that interfere with the periodic human motion count, or when adevice orientation is changed as a periodic human motion occurs. In oneembodiment, the exit mode assumes that a periodic human motion has beenmissed, so that if the exit mode determines that a user is stillcontinuing the same activity, the originally uncounted periodic humanmotion is not missed.

The process begins by initiating a timer (block 905). The timer measuresthe amount of time that has passed since a periodic human motion hasbeen identified. In one embodiment, the timer is a countdown timer thatterminates exit mode when the timer reaches zero. In one embodiment, thetimer starts counting when a cadence window minimum is reached, andstops counting when a cadence window maximum is reached. In analternative embodiment, the timer starts counting as soon as the exitmode is initiated, and stops counting when a cadence window maximum isreached.

At block 910, a periodic human motion is added to a buffered count. Atblock 915, processing logic determines whether the buffered count isequal to X, where X is the number of identified periodic human motionsthat, when reached, return processing logic to an appropriate activemode. In one embodiment, X is between 3 and 8. In one embodiment, thevalue of X is dependent upon the specific active mode that exit mode wasinitiated from. If the buffered count is equal to X, then the bufferedperiodic human motions are added to motion log and the previous(appropriate) active mode is reinitiated (block 920). If the bufferedcount is not equal to X, then processing logic proceeds to block 925.

At block 925, processing logic determines whether the timer has timedout (allotted time has elapsed). In one embodiment, the timer times outwhen no periodic human motions are counted within a cadence window. Inone embodiment, the timer times out when no periodic human motions arecounted in two or more cadence windows. If the allotted time haselapsed, then the buffered count is cleared, and entry mode is initiated(block 930). If the allotted time has not elapsed, then processing logicdetermines whether a negative event has occurred (block 932). If anegative event has occurred, the method returns to block 930. If nonegative event has occurred, the method proceeds to block 935.

At block 935, processing logic determines whether an additional periodichuman motion is recognized. If an additional periodic human motion isrecognized, then the timer is reset (block 905), the buffered count isincremented by one (block 910), and on the process continues to block915. If a periodic human motion is not recognized, then processing logicreturns to block 925 to determine whether the timer has elapsed.

FIG. 10 illustrates a flow diagram for a method 1000 of recognizing aperiodic human motion, in accordance with one embodiment of the presentinvention. In one embodiment, method 1000 may be executed by blocks 710and 724 of FIG. 7A, blocks 757 and 765 of FIG. 7B, block 815 of FIG. 8and block 935 of FIG. 9. In one embodiment, method 1000 is performed byelectronic device 100 of FIG. 1, or the motion identification system 200of FIG. 2.

Referring to FIG. 10, the process begins with measurements ofacceleration data being taken (block 1005). Measurements are takenaccording to a sampling rate, which may vary. In one embodiment, thesampling rate may range from one measurement per second to manymeasurements a second, depending on the operating mode being used.

At processing block 1010, in one embodiment measurements are filtered.Measurements can be filtered to remove high frequency data and/or lowfrequency data. In one embodiment, what data to filter depends on thetype of user activity being detected. At processing block 1012, in oneembodiment the inertial sensor is oriented by assigning a dominant axis.Assigning a dominant axis may include calculating rolling averages ofacceleration and assigning the dominant axis based on the rollingaverages of acceleration. Assigning the dominant axis may also includedetermining which axis has the largest peak to peak difference.

At block 1015, processing logic determines whether a measurement iswithin a cadence window. If the measurement is not within a cadencewindow, then no periodic human motion may be recognized or counted forthat measurement (block 1040). If the measurement is within the cadencewindow, the process continues to block 1017.

At block 1017, processing logic determines whether motion criteria aremet. For example, in one embodiment the process may determine whetheracceleration along the dominant axis is greater than a lower threshold.In one embodiment, the process may determine whether the measurementexceeds the rolling average by a set margin. In one embodiment,processing logic may determine whether acceleration along the dominantaxis is greater than previous measurements. Other criteria than thosementioned herein may also be used.

If all required motion criteria are met, then the appropriate periodichuman motions are counted (block 1035).

FIG. 11 illustrates a block diagram of a machine in the exemplary formof a computer system 1100 within which a set of instructions, forcausing the machine to perform any one or more of the methodologiesdiscussed herein, may be executed. The exemplary computer system 1100includes a processing device (processor) 1105, a memory 1110 (e.g.,read-only memory (ROM), a storage device, a static memory, etc.), and aninput/output 1115, which communicate with each other via a bus 1120.Embodiments of the present invention may be performed by the computersystem 1100, and/or by additional hardware components (not shown), ormay be embodied in machine-executable instructions, which may be used tocause processor 1105, when programmed with the instructions, to performthe method described above. Alternatively, the method may be performedby a combination of hardware and software.

Processor 1105 represents one or more general-purpose processing devicessuch as a microprocessor, central processing unit, or the like. Moreparticularly, the processor 1105 may be a complex instruction setcomputing (CISC) microprocessor, reduced instruction set computing(RISC) microprocessor, very long instruction word (VLIW) microprocessor,or a processor implementing other instruction sets or processorsimplementing a combination of instruction sets. The processor 1105 mayalso be one or more special-purpose processing devices such as anapplication specific integrated circuit (ASIC), a field programmablegate array (FPGA), a digital signal processor (DSP), network processor,or the like.

The present invention may be provided as a computer program product, orsoftware, that may be stored in memory 1110. Memory 1110 may include amachine-readable medium having stored thereon instructions, which may beused to program exemplary computer system 1100 (or other electronicdevices) to perform a process according to the present invention. Othermachine-readable mediums which may have instruction stored thereon toprogram exemplary computer system 1100 (or other electronic devices)include, but are not limited to, floppy diskettes, optical disks,CD-ROMs, and magneto-optical disks, ROMs, RAMs, EPROMs, EEPROMs,magnetic or optical cards, flash memory, or other type of media ormachine-readable mediums suitable for storing electronic instructions.

Input/output 1115 may provide communication with additional devicesand/or components. In one embodiment, input/output 1115 may transmitdata to and receive data from, for example, networked computers,servers, mobile devices, etc.

In the foregoing description, numerous specific details have been setforth such as examples of specific systems, languages, components, etc.in order to provide a thorough understanding of the present invention.It will be apparent, however, to one skilled in the art that thesespecific details need not be employed to practice the present invention.In other instances, well known materials or methods have not beendescribed in detail in order to avoid unnecessarily obscuring thepresent invention. The invention has been described with reference tospecific exemplary embodiments. It will, however, be evident thatvarious modifications and changes may be made thereto without departingfrom the broader spirit and scope of the invention as set forth in theappended claims. The specification and drawings are, accordingly, to beregarded in an illustrative rather than a restrictive sense.

What is claimed is:
 1. A method of monitoring human activity using aninertial sensor in a device carried by a user, the method comprising:monitoring, by the device, accelerations representing user movement ofthe user; calculating a user movement based on a plurality ofaccelerations over time; identifying one or more plurality of potentialactivities by matching the user movement to characteristics for each ofthe plurality of potential activities within a cadence window associatedwith the potential activities, where the cadence window for an activityis a length of time between a most recent periodic human motion and anext periodic human motion for the activity, and is set when a firstperiodic human motion is recognized, and where at least two of thepotential activities have non-identical movement characteristics; whenthere are a plurality of potential activities, counting movements foreach of the plurality of potential activities, and removing a particularpotential activity from the plurality of potential activities when theuser movement does not match the particular potential activity;identifying a current user activity from the plurality of potentialactivities based on the counted movements for the plurality of potentialactivities; and counting periodic human motions appropriate to thecurrent user activity and communicating, by the device carried by theuser, the counted periodic human motions appropriate to the currentactivity to the user.
 2. The method of claim 1, wherein the plurality ofuser activities comprises one or more of the following: walking, inlineskating, bicycling, exercising on an elliptical machine, exercising on arowing machine, and cross country skiing.
 3. The method of claim 1,further comprising, wherein the count for the potential activities thatare not the current activity are zeroed out when the current activity isidentified.
 4. The method of claim 1, further comprising: placing theinertial sensor in a sleep mode when no periodic human motions aredetected after a period of time.
 5. The method of claim 1, whereincounting movements for each of the plurality of potential activitiescomprises: permitting reentry to continue counting movements for each ofthe plurality of potential activities.
 6. The method of claim 1, whereinthe plurality of user activities comprises one or more of the following:walking and bicycling.
 7. An activity monitor comprising: an inertialsensor to detect user movements; a processor having a plurality ofmodes, the plurality of modes comprising: an entry mode, monitoring datafrom the inertial sensor, to determine whether the user movements meet aplurality of entry conditions associated with a particular activity, ofa plurality of potential activities, within a cadence window associatedwith the potential activities, where the cadence window for an activityis a length of time between a most recent periodic human motion and anext periodic human motion for the activity, and is set when a firstperiodic; human motion is recognized, and where at least two of thepotential activities have non-identical entry conditions; an activemode, entered when the user movements meet the plurality of entryconditions for a particular activity, in the active mode the processorcounting periodic human motions associated with the particular activityuntil a negative event occurs; an exit mode, entered when the negativeevent occurs, the exit mode permitting reentry into the active mode tocontinue counting periodic human motions associated with the particularhuman activity, when the periodic human motions match characteristicsassociated with the particular activity; and a mode of communicating thecounted periodic human motions appropriate to the particular activity tothe user via the activity monitor.
 8. The activity monitor of claim 7,further comprising: the processor to enter a sleep mode when no periodichuman motions are detected in the exit mode.
 9. The activity monitor ofclaim 8, further comprising: the processor to enter the entry mode whenperiodic human motions are detected in the sleep mode.
 10. The activitymonitor of claim 7, wherein the negative event comprises not detectingthe periodic human motions meeting criteria for the particular activityfor a period.
 11. The activity monitor of claim 7, wherein the count forthe potential activities that are not the current activity are zeroedout when the current activity is identified.
 12. The activity monitor ofclaim 7, wherein the plurality of user activities comprise one or moreof the following: walking, inline skating, bicycling, exercising on anelliptical machine, exercising on a rowing machine, and cross countryskiing.
 13. A method of identifying a particular activity for monitoringfrom among a plurality of potential activities comprising: receiving, bya device carried by the user, motion data from an inertial sensor in thedevice carried by a user; matching the motion data to a plurality ofsets of criteria within a cadence window associated with the potentialactivities, each set of criteria associated with one the plurality ofpotential activities, in a processor, where the cadence window for anactivity is a length of time between a most recent periodic human motionand a next periodic human motion for the activity, and is set when afirst periodic human motion is recognized, and where at least two of thepotential activities have non-identical sets of criteria; identifyingone or more of the plurality of potential activities whose criteria themotion data matches; counting periodic human motions for each of the oneor more plurality of potential activities, until the motion data matchesonly a particular activity; identifying the particular activity as acurrent activity, and continuing to count the periodic human motions forthe current activity; and communicating, by the device carried by theuser, the periodic human motions for the current activity to the user.14. The method of claim 13, wherein the count for the potentialactivities that are not the current activity are zeroed out when thecurrent activity is identified.
 15. The method of claim 13, wherein theplurality of user activities comprise one or more of the following:walking, inline skating, bicycling, exercising on an elliptical machine,exercising on a rowing machine, and cross country skiing.
 16. The methodof claim 13, further comprising: placing the processor into a sleep modewhen no periodic human motions are detected after a time.
 17. The methodof claim 16, further comprising: moving the processor to an entry modewhen periodic human motions are detected in a sleep mode.
 18. The methodof claim 16, further comprising: stopping counting the periodic humanmotions for a particular potential activity when a negative event isdetected for the particular potential activity.
 19. The method of claim18, wherein the negative event comprises not detecting the periodichuman motions meeting criteria for the particular activity for a period.20. The method of claim 13, wherein counting periodic human motions foreach of the one or more plurality of potential activities comprises:permitting reentry to continue counting periodic human motions for eachof the one or more plurality of potential activities, until the motiondata matches only a particular activity.